Prediction of Air Quality Index using Random Forest Algorithm

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Prediction of Air Quality Index using Random Forest Algorithm
Dipak Gaikar 1, Ujjwal Patel2, Om Vispute3,Sagar Singh4, Takshil Sanghvi5
1 Asst. Professor, Dept. of Computer Engineering, Rajiv Gandhi Institute of Technology, Maharashtra, India
2 B.E. student, Dept. of Computer Engineering, Rajiv Gandhi Institute of Technology, Maharashtra, India
--------------------------------------------------------------------------***-----------------------------------------------------------------------
Abstract - Air pollution is a growing concern
worldwide, and it has serious implications on human
health, the environment, and the economy. In this
project, we explore the prediction of Air Quality Index
(AQI) using the Random Forest algorithm. AQI is a
measure of air pollution that is used to communicate the
health risks associated with breathing polluted air. We
use historical data collected from various air quality
monitoring stations in a city and apply the Random
Forest algorithm to predict AQI. This study aims to
predict the AQI using machine learning algorithms. The
AQI is a crucial indicator of air quality, and accurate
forecasting can help mitigate the negative effects of air
pollution on human health and the environment. The
study utilizes data from air quality monitoring stations
and meteorological sensors to train and evaluate various
machine learning models, including Random Forest,
Support Vector Regression, and Artificial Neural
Networks. The accuracy of the algorithm is measured
using the root mean square error . The mean square
error and the mean absolute erro). The results indicate
that the Random Forest algorithm performs well in
predicting AQI and has the potential to be used as a tool
to monitor air quality and help in making decisions to
reduce air pollution. The findings of this study can be
used by policy makers, city planners, and environmental
agencies to design effective strategies to combat air
pollution.
Keywords: Prediction, Machine Learning, Random
Forest, Air Quality, P.M 2.5 , Root mean squared error(
RMSE), Mean Squared error(MSE),mean absolute
error (MAE).
1. INTRODUCTION
Air pollution is a pervasive problem that affects millions
of people worldwide, resulting in adverse health
outcomes, environmental degradation, and economic
losses. The World Health Organization (WHO) estimates
that air pollution causes around 7 million premature
deaths annually, making it one of the leading global
health risks (WHO, 2021). Air Quality Index (AQI) is a
measure of air pollution that provides information on
the air quality status and associated health risks. AQI is a
numerical value ranging from 0 to 500, and it is
calculated based on the levels of major air pollutants
such as particulate matter (PM), ozone (O3), nitrogen
dioxide (NO2), and sulfur dioxide (SO2).
Various approaches have been developed to monitor and
manage air quality, including regulatory policies,
emission controls, and air quality forecasting. Air quality
forecasting aims to predict future AQI levels using
statistical and machine learning models based on
historical data and meteorological factors. Machine
learning techniques such as Linear Regression, support
vector regression (SVR), and decision trees have been
applied to air quality forecasting . Random Forest (RF) is
a powerful machine learning algorithm that has been
used for AQI prediction in recent studies.
2. OBJECTIVE
• Air quality forecasting that uses machine learning to
predict the air quality index for a given region.
• To achieve better performance than the standard
regression models.
• Our goal is for the model to accurately predict Air
Quality Index for India as a whole.
• By forecasting Air Quality Index, we can track the
main pollutants causing pollutants and the locations
across India that are severely affected by pollutants.
• By creating a easily operated graphical user
interface we will help the user to keep a track of the
air quality index and its attribute on a single screen.
3. PROPOSED SYSTEM
AQI is an important environmental indicator that is used
to inform public health and policy decisions. The
proposed System using an Enhanced approach using
ANN (Artificial Neural Network) is tested using the
dataset of list 5 years (2013-2018). The results are
compared with previous methods results. These
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 10 Issue: 04 | Apr 2023 www.irjet.net p-ISSN: 2395-0072

methods are Random Forest, Linear regression, XG
boost , K Nearest Neighbour Regression, ANN .The
proposed enhanced method for AQI advantages over this
methods. When compared to various other methods, our
model gave the most precise forecasts. This technique
makes it simple and accurate for meteorologists to
forecast the weather and the AQI in the future. Fine
material (P.M 2.5) may be significant because, once its
level in the air is somewhat high, it poses a serious threat
to people's health. Small airborne particles ,known as PM
2.5,reduce visibility and high levels make the air look like
fog .
Fig -1 Proposed System Model
4. METHODOLOGY
4.1 Data sources
Collect data on various air quality parameters such as
particulate matter (PM10, PM2.5), sulfur dioxide (SO2),
nitrogen dioxide (NO2), ozone (O3), carbon monoxide
(CO), etc. for a given location at different times. This data
can be obtained of India from local environmental
agencies or online sources.
T TM Tm SLP H VV V VM PM 2.5
16.
9
25.
1
6.6 1021.
3
6
5
1.
1
2 7.6 284.795
8
15.
5
24.
1
7.7 1021 7
1
1.
1
3.
5
11.
1
219.720
8
14.
9
22.
8
8 1018.
4
7
3
1.
1
5.
9
13 182.187
5
18.
3
24.
7
11.
5
1018.
1
8
5
0.
5
1.
1
7.6 154.037
5
Table–1 Sample Data
4.2 Preprocessing of data
Clean the data and remove any missing or inconsistent
values. There are various techniques which are used in
data preprocessing i.e data cleaning , data integration &
data transformation, data reduction, data encoding. The
overall goal aim of data preprocessing is to insure that
the data is ready for analysis or machine learning and
that it will produce accurate and meaningful results.
4.3 Feature Selection
Select the relevant features from the dataset that can
impact air quality. This can be done using statistical
techniques or domain knowledge. There are several
techniques for feature selection, such as filter methods,
wrapper methods, and embedding methods. Filter
methods involve evaluating the relevance of each feature
based on some statistical measure, such as correlation or
mutual information, and selecting the top-ranked
features. Wrapper methods involve selecting features
based on the performance of a machine learning
algorithm, such as decision trees or SVM, with a
particular subset of features. Embedded methods involve
incorporating feature selection into the learning
algorithm itself, such as with regularization techniques
like Lasso or Ridge regression.
4.4 Train-Test Split
Train-test split is a technique used in machine learning
to evaluate the performance of a model on unseen data.
The process involves splitting a dataset into two parts: a
training set and a testing set. The training set is used to
train the model, and the testing set is used to evaluate
the model's performance. The goal is to train a model
that can generalize well to new, unseen data. The
splitting of the dataset can be done randomly or using a
specific technique such as stratified sampling, where the
split is done in a way that preserves the proportion of
classes or values in the original dataset.
Fig -2 Training and splitting data
4.5 Model Selection
Build a random forest model using the training data.
Random forest is an ensemble method that combines
multiple decision trees and reduces overfitting. It

belongs to the ensemble learning family of algorithms,
which combines multiple models to make better
predictions than any individual model. The basic idea
behind the random forest algorithm is to build a
collection of decision trees and combine their outputs to
make a final prediction. Each decision tree in the forest is
trained on a different subset of the original data and a
random subset of the features. By creating different trees
based on different data subsets and features, random
forest reduces the risk of overfitting and improves the
accuracy and stability of the predictions. When making a
prediction, each decision tree in the forest predicts the
outcome independently, and the final prediction is made
by combining the outputs of all the trees. In classification
tasks, the prediction is typically based on a majority vote
of the trees, while in regression tasks, the prediction is
typically based on the average of the outputs of the trees.
Fig -3 Selection of Model
4.6 Hyperparameter Tuning:
Hyperparameter tuning is an essential step in optimizing
the performance of a Random Forest model for air
quality index prediction. Here are the steps you can
follow for hyperparameter tuning in Random Forest:
1)Split the data: Divide your dataset into a training set
and a validation set. You can use a 70-30 split or a 80-20
split, depending on the size of your dataset.
2)Define hyperparameters: Select the hyperparameters
to tune. In Random Forest, some of the hyperparameters
that can be tuned include the number of trees in the
forest, the depth of each tree, the minimum number of
samples required to split an internal node, and the
maximum number of features to consider when looking
for the best split.
3)Choose a metric: Select a performance metric that you
want to optimize. For air quality index prediction, you
can use metrics like mean squared error (MSE), mean
absolute error (MAE), or R-squared (R2).
4)Grid search: Use a grid search to try out all possible
combinations of hyperparameters. Grid search is a
technique that allows you to define a big variety of utility
value for every hyperparameter and then conductes the
evaluation for the model for all possible combinations of
these values.
5) Cross-validation: Perform k-fold cross-validation on
each combination of hyperparameters to get a more
accurate estimate of the model's performance. Cross-
validation helps to reduce the risk of overfitting and
provides a more reliable estimate of the model's
performance.
6)Evaluate performance: After completing the grid
search and cross-validation, select the hyperparameters
that give the best performance on the validation set.
7)Test on new data: Finally, test the model with the
selected hyperparameters on a new test dataset to
evaluate its performance in real-world scenarios.
4.7 Model Evaluation
Random forest is a popular machine learning algorithm
used for regression and classification tasks. It is widely
used for air quality index prediction due to its ability to
handle non-linear relationships between the input
variables and the target variable. However, it is
important to evaluate the performance of the Random
Forest model to ensure its accuracy and reliability. some
commonly used evaluation metrics for a Random Forest
model:
1)Mean Squared Error (MSE): MSE measures the mean
squared difference between the predicted and actual
values. Lower values of MSE indicate better performance
of the model.
2) Mean Absolute Error(MAE): MAE measures the
average absolute difference between the predicted and
actual AQI values.
3) Root Mean Squared Error (RMSE): RMSE measures
the average squared difference between the predicted
and actual AQI values, and it takes the square root of the
result.

4)Rsquared(R^2): R-squared is a degree of the way
properly the version suits the data.. It measures the
proportion of the variance in the AQI values that can be
explained by the model. R-squared values range from 0
to 1, with a value of 1 indicating a perfect fit.
5. ARCHITECTURE
The figure below shows the system configuration of the
proposed system. To train the model first the dataset is
preprocessed. After pre-processing feature extraction is
done for the dataset from which we get training data.
These Training data are then passed into various data
science model. Next, you'll finally check the PM2.5
pollutant range predictions to predict whether the air
quality levels are good or good enough to deploy the
model. Otherwise, , you will have to redeploy the model
and dataset.
Fig -4 System Architecture
6. RESULT
In this project , we have shown how using Random
Forest Algorithm we have obtained precise and accurate
results for Air Quality Index . we have used parameters
such as MAE,MSE and RMSE.
MAE:
36.326655063
86365
MSE:
2704.4949219
76799
RMSE:
52.0047586474
23785
The below representation shows us the categorical
division by Environmental Protection Agency(EPA) for
AQI. Here using a Graphical User Interface(GUI),We have
established our results in the most simplest form using
random forest algorithm with the best accuraty we could
have achieved. The User Interface shows various fields
which helps us to find Air Quality Index based on the
data feeded in it.
Fig -5 Category division for AQI
Fig -6 GUI for the Output
Fig -7 GUI Information in the Output
Fig -8 GUI Information in the Output

Fig -9 AQI Prediction in GUI
7. CONCLUSIONS & FUTURE SCOPE
In conclusion, random forest is a powerful machine
learning algorithm that can be used for air quality index
prediction. It is a popular method for its ability to handle
complex, high-dimensional datasets and to identify
important features for prediction. By using random
forest to analyze various air quality parameters, such as
temperature, humidity, and particulate matter
concentrations, it is possible to accurately predict the air
quality index at a given location and time. However, it is
important to note that prediction accuracy can be
affected by the quality and quantity of data used to train
the model, as well as other external factors such as
weather conditions and human activity.
8. ACKNOWLEDGEMENT
This project was conducted and all the evaluations were
implemented under the guidance of Prof Dipak Gaikar ,
Department of Computer Engineering at MCT’S Rajiv
Gandhi Institute of Technology, Mumbai, India.
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